The present invention relates to systems and methods for object detection in videos.
In the field of computer vision object recognition describes the task of finding and identifying objects in an image or video sequence. Humans recognize a multitude of objects in images with little effort, despite the fact that the image of the objects may vary a lot depending on the viewpoint. Objects may need to be recognized when they are partially obstructed from view. This task is still a challenge for computer vision systems. To train accurate classifiers, large amounts of data are required.
Many approaches to the task have been implemented over multiple decades. Typically the training data is human labeled. To provide training examples, an operator would normally have to watch long hours of video until a sufficient number of labeled examples are obtained to train a classifier. However, conventional systems do not emphasize the training aspects of classifiers from the operator's standpoint.